Tootfinder

Encoding Numeric Computations and Infusing Heuristic Knowledge Using Integrity Constraints in stableKanren
Xiangyu Guo, Ajay Bansal
https://arxiv.org/abs/2510.04049 https://

Encoding Numeric Computations and Infusing Heuristic Knowledge Using Integrity Constraints in stableKanren
This paper presents examples of using integrity constraints in stableKanren to encode numeric computations for problem solving. Then, we use one of the examples to introduce multiple ways to infuse heuristic knowledge and reduce solving time. stableKanren is an extension of miniKanren that supports normal logic programs under stable model semantics. stableKanren further supports numeric computation by constructing a constraint store for integrity constraints. There are three ways to extend a re…

Set to Be Fair: Demographic Parity Constraints for Set-Valued Classification
Eyal Cohen (LPSM), Christophe Denis (SAMM), Mohamed Hebiri (LAMA)
https://arxiv.org/abs/2510.04926 h…

Set to Be Fair: Demographic Parity Constraints for Set-Valued Classification
Set-valued classification is used in multiclass settings where confusion between classes can occur and lead to misleading predictions. However, its application may amplify discriminatory bias motivating the development of set-valued approaches under fairness constraints. In this paper, we address the problem of set-valued classification under demographic parity and expected size constraints. We propose two complementary strategies: an oracle-based method that minimizes classification risk while…

Inverse Mixed-Integer Programming: Learning Constraints then Objective Functions
Akira Kitaoka
https://arxiv.org/abs/2510.04455 https://arxiv.org/pdf/2510.…

Inverse Mixed-Integer Programming: Learning Constraints then Objective Functions
In mixed-integer linear programming, data-driven inverse optimization that learns the objective function and the constraints from observed data plays an important role in constructing appropriate mathematical models for various fields, including power systems and scheduling. However, to the best of our knowledge, there is no known method for learning both the objective functions and the constraints. In this paper, we propose a two-stage method for a class of problems where the objective functio…

Volume-Based Lower Bounds to the Capacity of the Gaussian Channel Under Pointwise Additive Input Constraints
Neri Merhav (Shitz), Shlomo Shamai (Shitz)
https://arxiv.org/abs/2510.04095

Volume-Based Lower Bounds to the Capacity of the Gaussian Channel Under Pointwise Additive Input Constraints
We present a family of relatively simple and unified lower bounds on the capacity of the Gaussian channel under a set of pointwise additive input constraints. Specifically, the admissible channel input vectors $\bx = (x_1, \ldots, x_n)$ must satisfy $k$ additive cost constraints of the form $\sum_{i=1}^n ϕ_j(x_i) \le n Γ_j$, $j = 1,2,\ldots,k$, which are enforced pointwise for every $\bx$, rather than merely in expectation. More generally, we also consider cost functions that depend on a slid…

Feasibility-Aware Decision-Focused Learning for Predicting Parameters in the Constraints
Jayanta Mandi, Marianne Defresne, Senne Berden, Tias Guns
https://arxiv.org/abs/2510.04951

Feasibility-Aware Decision-Focused Learning for Predicting Parameters in the Constraints
When some parameters of a constrained optimization problem (COP) are uncertain, this gives rise to a predict-then-optimize (PtO) problem, comprising two stages -- the prediction of the unknown parameters from contextual information and the subsequent optimization using those predicted parameters. Decision-focused learning (DFL) implements the first stage by training a machine learning (ML) model to optimize the quality of the decisions made using the predicted parameters. When parameters in the…

from my link log —
Should CSS be a constraint system instead?
https://pavpanchekha.com/blog/why-css-bad.html
saved 2025-12-06 https://

Automaton Constrained Q-Learning
Anastasios Manganaris, Vittorio Giammarino, Ahmed H. Qureshi
https://arxiv.org/abs/2510.05061 https://arxiv.org/pdf/2510.0…

Automaton Constrained Q-Learning
Real-world robotic tasks often require agents to achieve sequences of goals while respecting time-varying safety constraints. However, standard Reinforcement Learning (RL) paradigms are fundamentally limited in these settings. A natural approach to these problems is to combine RL with Linear-time Temporal Logic (LTL), a formal language for specifying complex, temporally extended tasks and safety constraints. Yet, existing RL methods for LTL objectives exhibit poor empirical performance in compl…

TDCOSMO 2025 - Cosmological constraints from #StrongLensing time delays: https://www.aanda.org/articles/aa/full_html/2025/12/aa55801-25/aa55801-25.html -> A speed camera for the universe: https://www.u-tokyo.ac.jp/focus/en/press/z0508_00434.html - researchers exploit gravitational lensing to see how fast the universe is really expanding.

Optimal Regularization Under Uncertainty: Distributional Robustness and Convexity Constraints
Oscar Leong, Eliza O'Reilly, Yong Sheng Soh
https://arxiv.org/abs/2510.03464 ht…

Optimal Regularization Under Uncertainty: Distributional Robustness and Convexity Constraints
Regularization is a central tool for addressing ill-posedness in inverse problems and statistical estimation, with the choice of a suitable penalty often determining the reliability and interpretability of downstream solutions. While recent work has characterized optimal regularizers for well-specified data distributions, practical deployments are often complicated by distributional uncertainty and the need to enforce structural constraints such as convexity. In this paper, we introduce a frame…

Fascinated at how Wikidata's property constraints push particular models.
The capture of Maduro has different instance values, eg both "military intervention" and "kidnapping" (with a "disputed by" qualifier).
But the (disputed) inclusion then tries to push the use of perpetrator/victim values, rather than a neutral "participant".
(Wikidata property values have highly contextual meanings beyond simple labels... but editors are sti…

REN: Anatomically-Informed Mixture-of-Experts for Interstitial Lung Disease Diagnosis
Alec K. Peltekian, Halil Ertugrul Aktas, Gorkem Durak, Kevin Grudzinski, Bradford C. Bemiss, Carrie Richardson, Jane E. Dematte, G. R. Scott Budinger, Anthony J. Esposito, Alexander Misharin, Alok Choudhary, Ankit Agrawal, Ulas Bagci
https://arxiv.org/abs…

REN: Anatomically-Informed Mixture-of-Experts for Interstitial Lung Disease Diagnosis
Mixture-of-Experts (MoE) architectures have significantly contributed to scalable machine learning by enabling specialized subnetworks to tackle complex tasks efficiently. However, traditional MoE systems lack domain-specific constraints essential for medical imaging, where anatomical structure and regional disease heterogeneity strongly influence pathological patterns. Here, we introduce Regional Expert Networks (REN), the first anatomically-informed MoE framework tailored specifically for med…

JWST-TST DREAMS - NIRSpec/PRISM Transmission Spectroscopy of the Habitable Zone Planet TRAPPIST-1 e & Secondary Atmosphere Constraints for the Habitable Zone Planet TRAPPIST-1 e / The Photochemical Plausibility of Warm Exo-Titans Orbiting M Dwarf Stars: #TRAPPIST-1e, an Earth-sized, habitable-zone exoplanet: https://news.arizona.edu/news/new-look-trappist-1e-earth-sized-habitable-zone-exoplanet

Super Yang-Mills with partially non-linear extended supersymmetry
Fotis Farakos, Ulf Lindstr\"om
https://arxiv.org/abs/2510.05039 https://arxiv.org/pd…

Super Yang-Mills with partially non-linear extended supersymmetry
We discuss 4D N=2 non-abelian gauge theories where one supersymmetry is preserved while the other one is spontaneously broken and non-linearly realized. The goldstino resides in a Maxwell multiplet of the Bagger-Galperin type. We introduce appropriate constraints that eliminate the chiral N=1 superfield sector of the non-abelian N=2 multiplets and discuss the properties of the leading order Lagrangians.

The IEEE Signal Processing Society's Leading Role in Developing Standards for Computational Imaging and Sensing: Part II
Andreas Bathelt, Benjamin Deutschmann, Hyeon Seok Rou, Kuranage Roche Rayan Ranasinghe, Giuseppe Thadeu Freitas de Abreu, Peter Vouras
https://arxiv.org/abs/2510.04913

The IEEE Signal Processing Society's Leading Role in Developing Standards for Computational Imaging and Sensing: Part II
In every imaging or sensing application, the physical hardware creates constraints that must be overcome or they limit system performance. Techniques that leverage additional degrees of freedom can effectively extend performance beyond the inherent physical capabilities of the hardware. An example includes synchronizing distributed sensors so as to synthesize a larger aperture for remote sensing applications. An additional example is integrating the communication and sensing functions in a wire…

Investigating LLM Variability in Personalized Conversational Information Retrieval
Simon Lupart, Dani\"el van Dijk, Eric Langezaal, Ian van Dort, Mohammad Aliannejadi
https://arxiv.org/abs/2510.03795

Investigating LLM Variability in Personalized Conversational Information Retrieval
Personalized Conversational Information Retrieval (CIR) has seen rapid progress in recent years, driven by the development of Large Language Models (LLMs). Personalized CIR aims to enhance document retrieval by leveraging user-specific information, such as preferences, knowledge, or constraints, to tailor responses to individual needs. A key resource for this task is the TREC iKAT 2023 dataset, designed to evaluate personalization in CIR pipelines. Building on this resource, Mo et al. explored …

A reassessment of the role of high $x$ data on the MSHT global PDF fit
L. A. Harland-Lang, T. Cridge, M. Reader, R. S. Thorne
https://arxiv.org/abs/2510.03753 https://

A reassessment of the role of high $x$ data on the MSHT global PDF fit
We present updates within the MSHT global PDF fit that focus on the high $x$ region, and on improving our understanding of the interplay of various theoretical contributions and experimental constraints here. We revisit the question of target mass and higher twist corrections, considering their impact for the first time at approximate N$^3$LO order in a global PDF analysis. Their inclusion is found to be moderate but not negligible on both the PDFs and preferred value of the strong coupling. In…

Crosschecking Cosmic Distances from DESI BAO and DES SNe Points to Systematics
Mauricio Lopez-Hernandez, Eoin \'O Colg\'ain, Saeed Pourojaghi, M. M. Sheikh-Jabbari
https://arxiv.org/abs/2510.04179

Crosschecking Cosmic Distances from DESI BAO and DES SNe Points to Systematics
We perform a consistency check of DESI DR2 BAO constraints ($D_M/r_d, D_H/r_d)$ by reconstructing the same quantities from DES supernovae (SNe) in bins with the same effective redshift $z_{\textrm{eff}}$. We find that the ratio of $D_M/r_d$ values are consistent with a horizontal, thus confirming that the distance duality relation holds up to calibration. However, the $D_H/r_d$ ratio shows a decreasing trend with $z_{\textrm{eff}}$ at $2.3 σ$ to $2.5 σ$ that cannot be explained by physics. We…

Regular black holes and boson stars in semiclassical gravity
Irfan Javed, Viqar Husain
https://arxiv.org/abs/2510.03935 https://arxiv.org/pdf/2510.03935

Regular black holes and boson stars in semiclassical gravity
We use a Hamiltonian version of the semiclassical Einstein equation to study classical gravity coupled to a quantum scalar field with potential in spherical symmetry. The system is defined by effective constraints where the matter terms are replaced by their expectation values in a quantum state. For the static case, we find numerically that the resulting equations admit asymptotically flat, de Sitter, and anti-de Sitter boson star and regular black hole solutions. We also show that the Bardeen…

A Fixed Point Framework for the Existence of EFX Allocations
S. Rasoul Etesami
https://arxiv.org/abs/2510.04915 https://arxiv.org/pdf/2510.04915

A Fixed Point Framework for the Existence of EFX Allocations
We consider the problem of the existence of an envy-free allocation up to any good (EFX) for linear valuations and establish new results by connecting this problem to a fixed point framework. Specifically, we first use randomized rounding to extend the discrete EFX constraints into a continuous space and show that an EFX allocation exists if and only if the optimal value of the continuously extended objective function is nonpositive. In particular, we demonstrate that this optimization problem …

MPC strategies for density profile control with pellet fueling in nuclear fusion tokamaks under uncertainty
Christopher A. Orrico, Hari Prasad Varadarajan, Matthijs van Berkel, Lennard Ceelen, Thomas O. S. J. Bosman, W. P. M. H. Heemels, Dinesh Krishnamoorthy
https://arxiv.org/abs/2510.04784

MPC strategies for density profile control with pellet fueling in nuclear fusion tokamaks under uncertainty
Control of the density profile based on pellet fueling for the ITER nuclear fusion tokamak involves a multi-rate nonlinear system with safety-critical constraints, input delays, and discrete actuators with parametric uncertainty. To address this challenging problem, we propose a multi-stage MPC (msMPC) approach to handle uncertainty in the presence of mixed-integer inputs. While the scenario tree of msMPC accounts for uncertainty, it also adds complexity to an already computationally intensive …

Sometimes I understand why people design new standards or formats when the existing ones can’t be extended (whether due to practical constraints, authority, or bureaucracy) or simply aren’t flexible enough to evolve. I think the XKCD comic doesn’t include that aspect.
And yes, maybe new standards will appear.
#nocontext

Dissecting Larval Zebrafish Hunting using Deep Reinforcement Learning Trained RNN Agents
Raaghav Malik, Satpreet H. Singh, Sonja Johnson-Yu, Nathan Wu, Roy Harpaz, Florian Engert, Kanaka Rajan
https://arxiv.org/abs/2510.03699

Dissecting Larval Zebrafish Hunting using Deep Reinforcement Learning Trained RNN Agents
Larval zebrafish hunting provides a tractable setting to study how ecological and energetic constraints shape adaptive behavior in both biological brains and artificial agents. Here we develop a minimal agent-based model, training recurrent policies with deep reinforcement learning in a bout-based zebrafish simulator. Despite its simplicity, the model reproduces hallmark hunting behaviors -- including eye vergence-linked pursuit, speed modulation, and stereotyped approach trajectories -- that c…

Nystr\"om-Accelerated Primal LS-SVMs: Breaking the $O(an^3)$ Complexity Bottleneck for Scalable ODEs Learning
Weikuo Wang, Yue Liao, Huan Luo
https://arxiv.org/abs/2510.04094

Nyström-Accelerated Primal LS-SVMs: Breaking the $O(an^3)$ Complexity Bottleneck for Scalable ODEs Learning
A major problem of kernel-based methods (e.g., least squares support vector machines, LS-SVMs) for solving linear/nonlinear ordinary differential equations (ODEs) is the prohibitive $O(an^3)$ ($a=1$ for linear ODEs and 27 for nonlinear ODEs) part of their computational complexity with increasing temporal discretization points $n$. We propose a novel Nyström-accelerated LS-SVMs framework that breaks this bottleneck by reformulating ODEs as primal-space constraints. Specifically, we derive for t…

I still think Wirtz was offside and hearing the commentators say that the semi-automated offsides technology is not in the same room as VAR and that they have to relay back and forth the assisting player and scoring player's numbers... is bananas!
Different company because of partnership constraints, but the SAOT at other major tournaments is instantaneous. Now we eventually get an image, but not vision of the ball at the moment the image purports to represent. PGMOL incompetence👏 …

A Position- and Energy-Aware Routing Strategy for Subterranean LoRa Mesh Networks
Nalith Udugampola, Xiaoyu Ai, Binghao Li, Henry Gong, Aruna Seneviratne
https://arxiv.org/abs/2510.03714

A Position- and Energy-Aware Routing Strategy for Subterranean LoRa Mesh Networks
Although LoRa is predominantly employed with the single-hop LoRaWAN protocol, recent advancements have extended its application to multi-hop mesh topologies. Designing efficient routing for LoRa mesh networks remains challenging due to LoRa's low data rate and ALOHA-based MAC. Prior work often adapts conventional protocols for low-traffic, aboveground networks with strict duty cycle constraints or uses flooding-based methods in subterranean environments. However, these approaches inefficiently …

Boomerang Distillation Enables Zero-Shot Model Size Interpolation
Sara Kangaslahti, Nihal V. Nayak, Jonathan Geuter, Marco Fumero, Francesco Locatello, David Alvarez-Melis
https://arxiv.org/abs/2510.05064

Boomerang Distillation Enables Zero-Shot Model Size Interpolation
Large language models (LLMs) are typically deployed under diverse memory and compute constraints. Existing approaches build model families by training each size independently, which is prohibitively expensive and provides only coarse-grained size options. In this work, we identify a novel phenomenon that we call boomerang distillation: starting from a large base model (the teacher), one first distills down to a small student and then progressively reconstructs intermediate-sized models by re-in…

Diffraction-limited operation of micro-metalenses: fundamental bounds and designed rules for pixel integration
Nicolas Kossowski, Christina Kyrou, R\'emi Colom, Pierre-Marie Coulon, Virginie Br\"andli, Jean-Luc Reverchon, Samira Khadir, Patrice Genevet
https://arxiv.org/abs/2510.04154

Diffraction-limited operation of micro-metalenses: fundamental bounds and designed rules for pixel integration
Metasurfaces provide a compact, flexible, and reliable solution for controlling the wavefront of light. In imaging systems, micro-lens arrays are integrated with pixel matrices to reduce optical crosstalk, enhance photon collection efficiency, and improve spatial resolution. However, as the aperture size of the photonic devices decreases, fundamental limitations associated with diffraction emerge. Here, we theoretically analyze and experimentally demonstrate that these constraints also affect t…

Optimizing Phase-Scheduling with Throughput Trade-offs in AQFP Digital Circuits
Robert S. Aviles, Peter A. Beerel
https://arxiv.org/abs/2510.03956 https://…

Optimizing Phase-Scheduling with Throughput Trade-offs in AQFP Digital Circuits
Adiabatic Quantum-Flux-Parametron (AQFP) logic is a promising emerging superconducting technology for ultra-low power digital circuits, offering orders of magnitude lower power consumption than CMOS. However, AQFP scalability is challenged by excessive buffer overhead due to path balancing technology constraints. Addressing this, recent AQFP works have proposed design solutions to reduce path balancing overhead using phase-skipping and phase-alignment. Phase-skipping is a circuit-level techniqu…

Crosslisted article(s) found for math.MG. https://arxiv.org/list/math.MG/new
[1/1]:
- Optimal Regularization Under Uncertainty: Distributional Robustness and Convexity Constraints
Oscar Leong, Eliza O'Reilly, Yong Sheng Soh

Efficient Input-Constrained Impulsive Optimal Control of Linear Systems with Application to Spacecraft Relative Motion
Ethan Foss, Simone D'Amico
https://arxiv.org/abs/2510.03423

Efficient Input-Constrained Impulsive Optimal Control of Linear Systems with Application to Spacecraft Relative Motion
This work presents a novel algorithm for impulsive optimal control of linear time-varying systems with the inclusion of input magnitude constraints. Impulsive optimal control problems, where the optimal input solution is a sum of delta functions, are typically formulated as an optimization over a normed function space subject to integral equality constraints and can be efficiently solved for linear time-varying systems in their dual formulation. In this dual setting, the problem takes the form …

Today, I designed, built and launched a small software application within very strict financial, security, hardware and software constraints.
Then I helped people with all levels of tech skill, and different hardware and software configurations, get it running with their own setups.
That's a lot of corporate speak and being vague, but it feels good to have it out in the world helping people: letting them get a very specific thing done better.

PAD-TRO: Projection-Augmented Diffusion for Direct Trajectory Optimization
Jushan Chen, Santiago Paternain
https://arxiv.org/abs/2510.04436 https://arxiv.o…

PAD-TRO: Projection-Augmented Diffusion for Direct Trajectory Optimization
Recently, diffusion models have gained popularity and attention in trajectory optimization due to their capability of modeling multi-modal probability distributions. However, addressing nonlinear equality constraints, i.e, dynamic feasi- bility, remains a great challenge in diffusion-based trajectory optimization. Recent diffusion-based trajectory optimization frameworks rely on a single-shooting style approach where the denoised control sequence is applied to forward propagate the dynamical sy…

Learning Survival Models with Right-Censored Reporting Delays
Yuta Shikuri, Hironori Fujisawa
https://arxiv.org/abs/2510.04421 https://arxiv.org/pdf/2510.0…

Learning Survival Models with Right-Censored Reporting Delays
Survival analysis is a statistical technique used to estimate the time until an event occurs. Although it is applied across a wide range of fields, adjusting for reporting delays under practical constraints remains a significant challenge in the insurance industry. Such delays render event occurrences unobservable when their reports are subject to right censoring. This issue becomes particularly critical when estimating hazard rates for newly enrolled cohorts with limited follow-up due to admin…

Short-rate models with stochastic discontinuities: a PDE approach
Alessandro Calvia, Marzia De Donno, Chiara Guardasoni, Simona Sanfelici
https://arxiv.org/abs/2510.04289 https:…

Short-rate models with stochastic discontinuities: a PDE approach
With the reform of interest rate benchmarks, interbank offered rates (IBORs) like LIBOR have been replaced by risk-free rates (RFRs), such as the Secured Overnight Financing Rate (SOFR) in the U.S. and the Euro Short-Term Rate (\euro STR) in Europe. These rates exhibit characteristics like jumps and spikes that correspond to specific market events, driven by regulatory and liquidity constraints. To capture these characteristics, this paper considers a general short-rate model that incorporates …

This is the best critique of SF's Family Zoning Plan I've seen, from Fernando Martí, who knows his stuff.
“Zoning is not the primary constraint to construction today — not in SF where it costs $800,000 or more to build a unit of housing. Nor is environmental review or planning approvals. If these were the constraints to production, then we wouldn’t have tens of thousands of already approved market-rate units unable to get construction financing”

Multiplicative Turing Ensembles, Pareto's Law, and Creativity
Alexander Kolpakov, Aidan Rocke
https://arxiv.org/abs/2510.04167 https://arxiv.org/pdf/25…

Multiplicative Turing Ensembles, Pareto's Law, and Creativity
We study integer-valued multiplicative dynamics driven by i.i.d. prime multipliers and connect their macroscopic statistics to universal codelengths. We introduce the Multiplicative Turing Ensemble (MTE) and show how it arises naturally - though not uniquely - from ensembles of probabilistic Turing machines. Our modeling principle is variational: taking Elias' Omega codelength as an energy and imposing maximum entropy constraints yields a canonical Gibbs prior on integers and, by restriction, o…

Improving on the Pythia modelling of equal-scale multi-parton distribution functions
Oleh Fedkevych, Jonathan R. Gaunt, Seonagh Smith
https://arxiv.org/abs/2510.04554 https://…

Improving on the Pythia modelling of equal-scale multi-parton distribution functions
Multi-parton distribution functions (mPDFs) are non-perturbative objects that are important in the prediction of multiple scattering rates at hadron colliders. In the case where the scales associated with all partons in the mPDF are the same, we have two theoretical constraints on the mPDF. These are symmetry in exchange of the parton indices, and the number and momentum sum rules. In a previous publication (arXiv:2208.08197) we found that the equal-scale mPDFs from the Pythia model could not s…

Federated Computation of ROC and PR Curves
Xuefeng Xu, Graham Cormode
https://arxiv.org/abs/2510.04979 https://arxiv.org/pdf/2510.04979

Federated Computation of ROC and PR Curves
Receiver Operating Characteristic (ROC) and Precision-Recall (PR) curves are fundamental tools for evaluating machine learning classifiers, offering detailed insights into the trade-offs between true positive rate vs. false positive rate (ROC) or precision vs. recall (PR). However, in Federated Learning (FL) scenarios, where data is distributed across multiple clients, computing these curves is challenging due to privacy and communication constraints. Specifically, the server cannot access raw …

Testing black hole metrics with binary black hole inspirals
Zhe Zhao, Swarnim Shashank, Debtroy Das, Cosimo Bambi
https://arxiv.org/abs/2510.04703 https://…

Testing black hole metrics with binary black hole inspirals
Gravitational wave astronomy has opened an unprecedented window onto tests of gravity and fundamental physics in the strong-field regime. In this study, we examine a series of well-motivated deviations from the classical Kerr solution of General Relativity and employ gravitational wave data to place constraints on possible deviations from the Kerr geometry. The method involves calculating the phase of gravitational waves using the effective one-body formalism and then applying the parameterized…

Pete Hegseth, told soldiers under his command in Iraq to
ignore legal advice about when they were permitted to kill enemy combatants under their rules of engagement.
The anecdote is contained in a book Hegseth wrote last year in which he also repeatedly railed against the
constraints placed on “American warfighters” by the laws of war and the Geneva conventions.
Hegseth is currently under scrutiny for a 2 September attack on a boat purportedly carrying drugs in the Ca…

Model-Guided Microstimulation Steers Primate Visual Behavior
Johannes Mehrer, Ben Lonnqvist, Anna Mitola, Abdulkadir Gokce, Paolo Papale, Martin Schrimpf
https://arxiv.org/abs/2510.03684

Model-Guided Microstimulation Steers Primate Visual Behavior
Brain stimulation is a powerful tool for understanding cortical function and holds promise for therapeutic interventions in neuropsychiatric disorders. Initial visual prosthetics apply electric microstimulation to early visual cortex which can evoke percepts of simple symbols such as letters. However, these approaches are fundamentally limited by hardware constraints and the low-level representational properties of this cortical region. In contrast, higher-level visual areas encode more complex…

Flexible Locomotion Learning with Diffusion Model Predictive Control
Runhan Huang, Haldun Balim, Heng Yang, Yilun Du
https://arxiv.org/abs/2510.04234 https://

Flexible Locomotion Learning with Diffusion Model Predictive Control
Legged locomotion demands controllers that are both robust and adaptable, while remaining compatible with task and safety considerations. However, model-free reinforcement learning (RL) methods often yield a fixed policy that can be difficult to adapt to new behaviors at test time. In contrast, Model Predictive Control (MPC) provides a natural approach to flexible behavior synthesis by incorporating different objectives and constraints directly into its optimization process. However, classical …

Didn’t know these quotes, but I love writing code ina world of constraints.
(From “math with bad drawings”)

Sutskever: We need new inductive constraints.
Marcus: That means neurosymbolic architectures! He said it. I was right all along. Why didn't anybody listen to me, the modern Cassandra? Woe is me.

HEHA: Hierarchical Planning for Heterogeneous Multi-Robot Exploration of Unknown Environments
Longrui Yang, Yiyu Wang, Jingfan Tang, Yunpeng Lv, Shizhe Zhao, Chao Cao, Zhongqiang Ren
https://arxiv.org/abs/2510.04161

HEHA: Hierarchical Planning for Heterogeneous Multi-Robot Exploration of Unknown Environments
This paper considers the path planning problem for autonomous exploration of an unknown environment using multiple heterogeneous robots such as drones, wheeled, and legged robots, which have different capabilities to traverse complex terrains. A key challenge there is to intelligently allocate the robots to the unknown areas to be explored and determine the visiting order of those spaces subject to traversablity constraints, which leads to a large scale constrained optimization problem that nee…

You may know the project management triangle.
https://en.wikipedia.org/wiki/Project_management_triangle
It puts the quality of a product in the center and pictures the scope, budget and time as constraints.
There is this other triagle I see for companies:

Calm local optimality for couple-constrained minimax problems
Xiaoxiao Ma, Jane Ye
https://arxiv.org/abs/2510.03861 https://arxiv.org/pdf/2510.03861…

Calm local optimality for couple-constrained minimax problems
Recently, a new local optimality concept for minimax problems, termed calm local minimax points, has been introduced. In this paper, we extend this concept to a general class of nonsmooth, nonconvex nonconcave minimax problems with coupled constraints, where the inner feasible set depends on the outer variable. We derive comprehensive first-order and second-order necessary and sufficient optimality conditions for calm local minimax points in the setting of nonsmooth, nonconvex nonconcave minima…

Integrated Planning and Control on Manifolds: Factor Graph Representation and Toolkit
Peiwen Yang, Weisong Wen, Runqiu Yang, Yuanyuan Zhang, Jiahao Hu, Yingming Chen, Naigui Xiao, Jiaqi Zhao
https://arxiv.org/abs/2510.04278

Integrated Planning and Control on Manifolds: Factor Graph Representation and Toolkit
Model predictive control (MPC) faces significant limitations when applied to systems evolving on nonlinear manifolds, such as robotic attitude dynamics and constrained motion planning, where traditional Euclidean formulations struggle with singularities, over-parameterization, and poor convergence. To overcome these challenges, this paper introduces FactorMPC, a factor-graph based MPC toolkit that unifies system dynamics, constraints, and objectives into a modular, user-friendly, and efficient …

Agile Tradespace Exploration for Space Rendezvous Mission Design via Transformers
Yuji Takubo, Daniele Gammelli, Marco Pavone, Simone D'Amico
https://arxiv.org/abs/2510.03544

Agile Tradespace Exploration for Space Rendezvous Mission Design via Transformers
Spacecraft rendezvous enables on-orbit servicing, debris removal, and crewed docking, forming the foundation for a scalable space economy. Designing such missions requires rapid exploration of the tradespace between control cost and flight time across multiple candidate targets. However, multi-objective optimization in this setting is challenging, as the underlying constraints are often highly nonconvex, and mission designers must balance accuracy (e.g., solving the full problem) with efficienc…

Performance-guided Task-specific Optimization for Multirotor Design
Etor Arza, Welf Rehberg, Philipp Weiss, Mihir Kulkarni, Kostas Alexis
https://arxiv.org/abs/2510.04724 https:…

Performance-guided Task-specific Optimization for Multirotor Design
This paper introduces a methodology for task-specific design optimization of multirotor Micro Aerial Vehicles. By leveraging reinforcement learning, Bayesian optimization, and covariance matrix adaptation evolution strategy, we optimize aerial robot designs guided exclusively by their closed-loop performance in a considered task. Our approach systematically explores the design space of motor pose configurations while ensuring manufacturability constraints and minimal aerodynamic interference. R…

ChatGPT Atlas hands-on: generally able to interpret instructions and navigate simple menus, but "technical constraints on session length" are a limiting factor (Kyle Orland/Ars Technica)
https://arstechnica.com/features/2025/

We let OpenAI’s “Agent Mode” surf the web for us—here’s what happened
From scanning emails to building fansites, Atlas can ably automate some web-based tasks.

Ukrainian Forces in Mirnohrad face operational encirclement amid severe logistics constraints, MP says: https://benborges.xyz/2025/12/23/ukrainian-forces-in-mirnohrad-face.html

I think we can actually prove that this constraint is the *only* constraint that can preserve freedom:
1. There will exist actors in a system who will wish to take advantage of others. Evolution drives survival and one strategy for increasing survival in an altruistic society is to become a parasite.
2. Expecting exploitative dynamics, a system needs to have a set of rules to manage exploitation.
3. If the set of rules is static it will lack the requisite variety necessary to manage the infinite possible behavior of humans so the system will fail.
4. If the system is dynamic then it must have a rule set about how it's own rules are updated. This would make the system recursive, which makes the system at least as complex as mathematics. Any system at least as complex as mathematics is necessarily either incomplete or inconsistent (Gödel's incompleteness theorem). If the system is incomplete, then constraints can be evaded which then allow a malicious agent to seize control of the system and update the rules for their own benefit. If constraints are incomplete, then a malicious agent can take advantage of others within the system.
5. Therefore, no social system can possibly protect freedom unless there exists a single metasystemic constraint (that the system must be optional) allowing for the system to be abandoned when compromised.
Oh, you might say, but this just means you have to infinitely abandon systems. Sure, but there's an evolutionary advantage to cooperation so there's evolutionary pressure to *not* be a malicious actor. So a malicious actor being able to compromise the whole system is likely to be a much more rare event. Compromising a system is a lot of work, so the first thing a malicious actor would want to do is preserve that work. They would want to lock you in. The most important objective to a malicious actor compromising a system would be to violate that metasystemic constraint, or all of their work goes out the window when everyone leaves.
And now you understand why borders exist, why fascists are obsessed with maintaining categories like gender, race, ethnicity, etc. This is why even Democrats like Newsom are on board with putting houseless people in concentration camps. And this is why the most important thing anarchists promote is the ability to choose not to be part of any of that.

Modeling and Optimization of Control Problems on GPUs
Alexis Montoison, Jean-Baptiste Caillau
https://arxiv.org/abs/2510.03932 https://arxiv.org/pdf/2510.0…

Modeling and Optimization of Control Problems on GPUs
We present a fully Julia-based, GPU-accelerated workflow for solving large-scale sparse nonlinear optimal control problems. Continuous-time dynamics are modeled and then discretized via direct transcription with OptimalControl.jl into structured sparse nonlinear programs. These programs are compiled into GPU kernels using ExaModels.jl, leveraging SIMD parallelism for fast evaluation of objectives, constraints, gradients, Jacobians and Hessians. The resulting sparse problems are solved entirely …

Manchester’s Local Plan – responding to the consultation
Local Plans are what guide the pattern of a land use and and building across a council area.  In the case of Greater Manchester (excluding Stockport), the Joint Strategic Plan, Places for Everyone sets the scene, as does the government's National Planning Policy Framework.  That means that there are some very real constraints on what can be put into a so-called Local Plan (not very local - in the case of…

Manchester’s Local Plan – responding to the consultation
UPDATE!  11 Nov.  We have now submitted a revised version.  It adds some answers to questions we didn’t address in the previously published draft, and has some additions to some of the other …

Well-Posedness and Efficient Algorithms for Inverse Optimal Transport with Bregman Regularization
Chenglong Bao, Zanyu Li, Yunan Yang
https://arxiv.org/abs/2510.03803 https://…

Well-Posedness and Efficient Algorithms for Inverse Optimal Transport with Bregman Regularization
This work analyzes the inverse optimal transport (IOT) problem under Bregman regularization. We establish well-posedness results, including existence, uniqueness (up to equivalence classes of solutions), and stability, under several structural assumptions on the cost matrix. On the computational side, we investigate the existence of solutions to the optimization problem with general constraints on the cost matrix and provide a sufficient condition guaranteeing existence. In addition, we propose…

How constraints fuel creativity!
https://www.youtube.com/watch?v=HoUMdM3x5Dw

Robust Closed-Form Control for MIMO Nonlinear Systems under Conflicting Time-Varying Hard and Soft Constraints
Farhad Mehdifar, Charalampos P. Bechlioulis, Dimos V. Dimarogonas
https://arxiv.org/abs/2510.11393

Robust Closed-Form Control for MIMO Nonlinear Systems under Conflicting Time-Varying Hard and Soft Constraints
This paper introduces a novel robust closed-form control law to handle time-varying hard and soft constraints in uncertain high-relative-degree nonlinear MIMO systems. These constraints represent spatiotemporal specifications in mechanical systems' operational space, with hard constraints ensuring safety-critical requirements and soft constraints encoding performance or task objectives. Initially, all constraints are consolidated into two separate scalar time-varying hard and soft constraint fu…

Oof, fuck microsoft.
But also, there's no reason to use permissive licenses any more. "Open source" has taken over the world. We no longer have to work with billion-dollar companies in order to get our stuff used. Now we need to protect ourselves from being taken advantage of by them.
https://

“Israeli soldiers have described a free-for-all in Gaza and a breakdown in norms and legal constraints, with civilians killed at the whim of individual officers, according to testimony in a TV documentary.
“If you want to shoot without restraint, you can,” Daniel, the commander of an Israel Defense Forces (IDF) tank unit, says in Breaking Ranks: Inside Israel’s War, due to be broadcast in the UK on ITV on Monday evening.
Some of the IDF soldiers who talked to the programme reques…

Remoteness, order, size and connectivity constraints in digraphs
Sufiyan Mallu
https://arxiv.org/abs/2510.08841 https://arxiv.org/pdf/2510.08841

Remoteness, order, size and connectivity constraints in digraphs
Let \( D \) be a strongly connected digraph. The average distance of a vertex \( v \) in \( D \) is defined as the arithmetic mean of the distances from \( v \) to all other vertices in \( D \). The remoteness \( ρ(D) \) of \( D \) is the maximum of the average distances of the vertices in \( D \). In this paper, we provide a sharp upper bound on the remoteness of a strong digraph with given order, size, and vertex-connectivity. We then characterise the extremal digraphs that maximise remote…

"In summary, #AGI, as commonly conceived, will not happen because it ignores the physical constraints of computation, the exponential costs of linear progress, and the fundamental limits we are already encountering. Superintelligence is a fantasy because it assumes that intelligence can recursively self-improve without bound, ignoring the physical and economic realities that constrain all systems. …

Constraint-Guided Unit Test Generation for Machine Learning Libraries
Lukas Krodinger, Altin Hajdari, Stephan Lukasczyk, Gordon Fraser
https://arxiv.org/abs/2510.09108 https://

Constraint-Guided Unit Test Generation for Machine Learning Libraries
Machine learning (ML) libraries such as PyTorch and TensorFlow are essential for a wide range of modern applications. Ensuring the correctness of ML libraries through testing is crucial. However, ML APIs often impose strict input constraints involving complex data structures such as tensors. Automated test generation tools such as Pynguin are not aware of these constraints and often create non-compliant inputs. This leads to early test failures and limited code coverage. Prior work has investig…

Industry executives say nearly all major memory chipmakers are running at or near full capacity, with 2026 production slots almost "sold out" due to AI demand (Nikkei Asia)
https://asia.nikkei.com/business/technolog…

Rampant AI demand throws the memory chip market into turmoil
Supply constraints spark warnings of price rises and launch delays for consumer electronics

Constraints on dark matter models from the stellar cores observed in ultra-faint dwarf galaxies: Self-interacting dark matter
Jorge Sanchez Almeida (Instituto de Astrofisica de Canarias, La Laguna, Tenerife, E-38200, Spain, Departamento de Astrofisica, Universidad de La Laguna, Spain)
https://arxiv.org/abs/2510.05682

Constraints on dark matter models from the stellar cores observed in ultra-faint dwarf galaxies: Self-interacting dark matter
It has been proposed that the stellar cores observed in ultra-faint dwarf (UFD) galaxies reflect underlying dark matter (DM) cores that cannot be formed by stellar feedback acting on collisionless cold dark matter (CDM) halos. Assuming this claim is correct, we investigate the constraints that arise if such cores are produced by self-interacting dark matter (SIDM). We derive the range of SIDM cross-sections (sigma/m) required to reproduce the observed core sizes. These can result from halos in …

Because #accessibility is out of scope for Baseline, devs may not know how poorly supported a feature is and miss an opportunity to vote for things that are barriers to users and risks to orgs.
“Vote for the web features you want to see”
https://

🌊 Offshore Pumped Hydro Could Solve Clean Energy's Biggest Problem
https://oilprice.com/Energy/Energy-General/Offshore-Pumped-Hydro-Could-Solve-Clean-Energys-Biggest-Problem.html

Offshore Pumped Hydro Could Solve Clean Energy's Biggest Problem
An Italian startup is developing an offshore pumped hydro storage system that avoids the environmental and geographical constraints of traditional land-based dams, offering a potentially disruptive solution for long-duration energy storage and grid stability.

I had missed that last month, the condo development in the south parking lot of Fairview Mall was scaled back (at least for the first phase)
It's now just 1 tower instead of 3, and it's a rental instead of condos.
It's a really bad market to sell condos right now, so it's not too surprising.

At what point does anyone think it will be economically feasible for a government to speculatively decode bulk intercepts of circa 2025 traffic (using, say, 2048 bit RSA keys) using quantum computers?
As in you've got 100k qubits you're not otherwise using, and no real energy constraints.
Because I've seen that used as reason not to allow Royal Mint Court to be used by China.

Low-redshift constraints on structure growth from CMB lensing tomography
Andrea Rubiola, Matteo Zennaro, Carlos Garc\'ia-Garc\'ia, David Alonso
https://arxiv.org/abs/2510.09563

Low-redshift constraints on structure growth from CMB lensing tomography
We present constraints on the amplitude of matter fluctuations from the clustering of galaxies and their cross-correlation with the gravitational lensing convergence of the cosmic microwave background (CMB), focusing on low redshifts ($z\lesssim0.3$), where potential deviations from a perfect cosmological constant dominating the growth of structure could be more prominent. Specifically, we make use of data from the 2MASS photometric survey (\tmpz) and the \wisc galaxy survey, in combination wit…

The Atacama #Cosmology Telescope: DR6 maps / power spectra, likelihoods and ΛCDM parameters / constraints on extended cosmological models: https://iopscience.iop.org/article/10.1088/1475-7516/2025/11/061 / https://iopscience.iop.org/article/10.1088/1475-7516/2025/11/062 / https://iopscience.iop.org/article/10.1088/1475-7516/2025/11/063 -> ACT releases its final data, shaping the future of cosmology: https://www.eurekalert.org/news-releases/1106637

NaViL: Rethinking Scaling Properties of Native Multimodal Large Language Models under Data Constraints
Changyao Tian, Hao Li, Gen Luo, Xizhou Zhu, Weijie Su, Hanming Deng, Jinguo Zhu, Jie Shao, Ziran Zhu, Yunpeng Liu, Lewei Lu, Wenhai Wang, Hongsheng Li, Jifeng Dai
https://arxiv.org/abs/2510.08565 …

NaViL: Rethinking Scaling Properties of Native Multimodal Large Language Models under Data Constraints
Compositional training has been the de-facto paradigm in existing Multimodal Large Language Models (MLLMs), where pre-trained vision encoders are connected with pre-trained LLMs through continuous multimodal pre-training. However, the multimodal scaling property of this paradigm remains difficult to explore due to the separated training. In this paper, we focus on the native training of MLLMs in an end-to-end manner and systematically study its design space and scaling property under a practica…

Fair Rent Division: New Budget and Rent Constraints
Rohith Reddy Gangam, Shayan Taherijam, Vijay V. Vazirani
https://arxiv.org/abs/2510.05434 https://arxiv…

Fair Rent Division: New Budget and Rent Constraints
We study the classical rent division problem, where $n$ agents must allocate $n$ indivisible rooms and split a fixed total rent $R$. The goal is to compute an envy-free (EF) allocation, where no agent prefers another agent's room and rent to their own. This problem has been extensively studied under standard assumptions, where efficient algorithms for computing EF allocations are known. We extend this framework by introducing two practically motivated constraints: (i) lower and upper bounds o…

Multi-Armed Bandits with Minimum Aggregated Revenue Constraints
Ahmed Ben Yahmed, Hafedh El Ferchichi, Marc Abeille, Vianney Perchet
https://arxiv.org/abs/2510.12523 https://

Multi-Armed Bandits with Minimum Aggregated Revenue Constraints
We examine a multi-armed bandit problem with contextual information, where the objective is to ensure that each arm receives a minimum aggregated reward across contexts while simultaneously maximizing the total cumulative reward. This framework captures a broad class of real-world applications where fair revenue allocation is critical and contextual variation is inherent. The cross-context aggregation of minimum reward constraints, while enabling better performance and easier feasibility, intro…

Physics-Informed Reinforcement Learning for Large-Scale EV Smart Charging Considering Distribution Network Voltage Constraints
Stavros Orfanoudakis, Frans Oliehoek, Peter Palesnky, Pedro P. Vergara
https://arxiv.org/abs/2510.12335

Physics-Informed Reinforcement Learning for Large-Scale EV Smart Charging Considering Distribution Network Voltage Constraints
Electric Vehicles (EVs) offer substantial flexibility for grid services, yet large-scale, uncoordinated charging can threaten voltage stability in distribution networks. Existing Reinforcement Learning (RL) approaches for smart charging often disregard physical grid constraints or have limited performance for complex large-scale tasks, limiting their scalability and real-world applicability. This paper introduces a physics-informed (PI) RL algorithm that integrates a differentiable power flow m…

from my link log —
Let's take esoteric programming languages seriously.
https://arxiv.org/abs/2505.15327
saved 2025-10-11 https://dotat.at/:/XKTKR.…

Let's Take Esoteric Programming Languages Seriously
Esoteric programming languages are challenging to learn, but their unusual features and constraints may serve to improve programming ability. From languages designed to be intentionally obtuse (e.g. INTERCAL) to others targeting artistic expression (e.g. Piet) or exploring the nature of computation (e.g. Fractan), there is rich variety in the realm of esoteric programming languages. This essay examines the counterintuitive appeal of esoteric languages and seeks to analyse reasons for this popul…

Localized structures in two-field systems: exact solutions in the presence of Lorentz symmetry breaking and explicit connection with geometric constraints
G. H. Bandeira, D. Bazeia, G. S. Santiago, Ya. Shnir
https://arxiv.org/abs/2510.07012

Localized structures in two-field systems: exact solutions in the presence of Lorentz symmetry breaking and explicit connection with geometric constraints
We investigate a class of models described by two real scalar fields in two-dimensional spacetime. The study focuses mainly on the presence of exact static solutions which satisfy the first-order formalism, in models constructed to engender Lorentz symmetry violation. We start by exploring a direct connection between Lorentz breaking and geometric constraint, as experimentally examined in the case of domain walls in geometrically constrained magnetic materials. By means of a specific choice of …

A Systematic Literature Review of Machine Learning Techniques for Observational Constraints in Cosmology
Luis Rojas, Sebasti\'an Espinoza, Esteban Gonz\'alez, Carlos Maldonado, Fei Luo
https://arxiv.org/abs/2510.09876

A Systematic Literature Review of Machine Learning Techniques for Observational Constraints in Cosmology
This paper presents a systematic literature review focusing on the application of machine learning techniques for deriving observational constraints in cosmology. The goal is to evaluate and synthesize existing research to identify effective methodologies, highlight gaps, and propose future research directions. Our review identifies several key findings: (1) various machine learning techniques, including Bayesian neural networks, Gaussian processes, and deep learning models, have been applied t…

Dynamic Regret Bounds for Online Omniprediction with Long Term Constraints
Yahav Bechavod, Jiuyao Lu, Aaron Roth
https://arxiv.org/abs/2510.07266 https://a…

Dynamic Regret Bounds for Online Omniprediction with Long Term Constraints
We present an algorithm guaranteeing dynamic regret bounds for online omniprediction with long term constraints. The goal in this recently introduced problem is for a learner to generate a sequence of predictions which are broadcast to a collection of downstream decision makers. Each decision maker has their own utility function, as well as a vector of constraint functions, each mapping their actions and an adversarially selected state to reward or constraint violation terms. The downstream dec…

Observational Constraints on Chaplygin Gas Models in Non-Minimally Coupled Power Law $f(Q)$ Gravity with Quasars
Nakul Aggarwal, Ali Pourmand, Fatimah Shojai, Harish Parthasarathy
https://arxiv.org/abs/2510.12472

Observational Constraints on Chaplygin Gas Models in Non-Minimally Coupled Power Law $f(Q)$ Gravity with Quasars
In the framework of $f(Q)$ gravity, where gravity emerges from non-metricity $Q$, we explore the cosmological implications of its non-minimal coupling to matter. Inspired by the recent success of Chaplygin gas models in explaining dark energy, we consider a background fluid composed of baryonic matter, radiation, and a family of Chaplygin gas variants namely Generalized Chaplygin Gas (GCG), Modified Chaplygin Gas (MCG), and Variable Chaplygin Gas (VCG). We constrain these models with three rece…

Sequential Convex Programming for 6-DoF Powered Descent Guidance with Continuous-Time Compound State-Triggered Constraints
Samet Uzun, Behcet Acikmese, John M. Carson III
https://arxiv.org/abs/2510.09610

Sequential Convex Programming for 6-DoF Powered Descent Guidance with Continuous-Time Compound State-Triggered Constraints
This paper presents a sequential convex programming (SCP) framework for ensuring the continuous-time satisfaction of compound state-triggered constraints, a subset of logical specifications, in the powered descent guidance (PDG) problem. The proposed framework combines the generalized mean-based smooth robustness measure (D-GMSR), a parameterization technique tailored for expressing discrete-time temporal and logical specifications through smooth functions, with the continuous-time successive c…

First simultaneous global QCD analysis of kaon and pion parton distributions with lattice QCD constraints
P. C. Barry, Chueng-Ryong Ji, W. Melnitchouk, N. Sato, Fernanda Steffens
https://arxiv.org/abs/2510.11979

First simultaneous global QCD analysis of kaon and pion parton distributions with lattice QCD constraints
We perform the first simultaneous global QCD analysis of pion and kaon parton distribution functions (PDFs), constrained by pion- and kaon-induced Drell-Yan (DY) and leading neutron electroproduction data, together with lattice QCD data on pion and kaon PDF moments. The analysis indicates a softer valence $\bar u$ distribution in the $K^-$ than in the $π^-$, and a significantly more peaked valence $s$-quark density in $K^-$ compared with the $\bar u$. The effective exponent governing the high-…

Bridging the Physics-Data Gap with FNO-Guided Conditional Flow Matching: Designing Inductive Bias through Hierarchical Physical Constraints
Tsuyoshi Okita
https://arxiv.org/abs/2510.08295

Bridging the Physics-Data Gap with FNO-Guided Conditional Flow Matching: Designing Inductive Bias through Hierarchical Physical Constraints
Conventional time-series generation often ignores domain-specific physical constraints, limiting statistical and physical consistency. We propose a hierarchical framework that embeds the inherent hierarchy of physical laws-conservation, dynamics, boundary, and empirical relations-directly into deep generative models, introducing a new paradigm of physics-informed inductive bias. Our method combines Fourier Neural Operators (FNOs) for learning physical operators with Conditional Flow Matching (C…

Orientation Learning and Adaptation towards Simultaneous Incorporation of Multiple Local Constraints
Gaofeng Li, Peisen Xu, Ruize Wang, Qi Ye, Jiming Chen, Dezhen Song, Yanlong Huang
https://arxiv.org/abs/2510.07986

Orientation Learning and Adaptation towards Simultaneous Incorporation of Multiple Local Constraints
Orientation learning plays a pivotal role in many tasks. However, the rotation group SO(3) is a Riemannian manifold. As a result, the distortion caused by non-Euclidean geometric nature introduces difficulties to the incorporation of local constraints, especially for the simultaneous incorporation of multiple local constraints. To address this issue, we propose the Angle-Axis Space-based orientation representation method to solve several orientation learning problems, including orientation adap…

Big Bang Nucleosynthesis constraints on space-time noncommutativity
Teodora Maria Matei, Cristian Croitoru, Tiberiu Harko
https://arxiv.org/abs/2510.10685 https://

Big Bang Nucleosynthesis constraints on space-time noncommutativity
We consider the implications of the modified dispersion relations, due to the noncommutativity of the spacetime, for a photon gas filling the early Universe in the framework of the Big Bang Nucleosynthesis (BBN) processes, during the period of light elements formation. We consider three types of deformations present in the dispersion relations for the radiation gas, from which we obtain the low temperature corrections to the energy density and pressure. The cosmological implications of the modi…

Updated constraints on interacting dark energy: A comprehensive analysis using multiple CMB probes, DESI DR2, and supernovae observations
Tian-Nuo Li, Guo-Hong Du, Yun-He Li, Yichao Li, Jia-Le Ling, Jing-Fei Zhang, Xin Zhang
https://arxiv.org/abs/2510.11363

Updated constraints on interacting dark energy: A comprehensive analysis using multiple CMB probes, DESI DR2, and supernovae observations
Recent DESI baryon acoustic oscillation (BAO) measurements, combined with Planck cosmic microwave background (CMB) data and DESY5 type Ia supernova (SN) data, indicate a significant deviation from $Λ$CDM, which seems to suggest that this deviation can be explained by an interaction between dark energy and dark matter. In this work, we perform a comprehensive analysis by utilizing the latest DESI DR2 BAO data in conjunction with CMB data from ACT, SPT, Planck, and WMAP, along with SN data from …

Pricing Short-Circuit Current via a Primal-Dual Formulation for Preserving Integrality Constraints
Peng Wang, Luis Badesa
https://arxiv.org/abs/2510.05293 https://

Pricing Short-Circuit Current via a Primal-Dual Formulation for Preserving Integrality Constraints
Synchronous Generators (SGs) currently provide important levels of Short-Circuit Current (SCC), a critical ancillary service that ensures line protections trip during short-circuit faults. Given the ongoing replacement of SGs by power-electronics-based generation, which have a hard limit for current injection, it has become relevant to optimize the procurement of SCC provided by remaining SGs. Pricing this service is however challenging due to the integrality constraints in Unit Commitment (UC)…

Constraints on the interacting holographic dark energy models: implications from background and perturbations data
N. Nazari Pooya
https://arxiv.org/abs/2510.08875 https://

Constraints on the interacting holographic dark energy models: implications from background and perturbations data
In this study, we employ a two-step method to analyze models of holographic dark energy (HDE) and interacting holographic dark energy (IHDE), incorporating three distinct dark energy (DE)-dark matter (DM) interaction terms. First, using the latest background dataset, we conduct a Markov chain Monte Carlo (MCMC) analysis to constrain the free parameters of the models. Then, we assess the models against each other using the key background parameters and compare them to the $Λ$CDM standard model.…

EB-MBD: Emerging-Barrier Model-Based Diffusion for Safe Trajectory Optimization in Highly Constrained Environments
Raghav Mishra, Ian R. Manchester
https://arxiv.org/abs/2510.07700

EB-MBD: Emerging-Barrier Model-Based Diffusion for Safe Trajectory Optimization in Highly Constrained Environments
We propose enforcing constraints on Model-Based Diffusion by introducing emerging barrier functions inspired by interior point methods. We show that constraints on Model-Based Diffusion can lead to catastrophic performance degradation, even on simple 2D systems due to sample inefficiency in the Monte Carlo approximation of the score function. We introduce Emerging-Barrier Model-Based Diffusion (EB-MBD) which uses progressively introduced barrier constraints to avoid these problems, significantl…

Entropy Regularizing Activation: Boosting Continuous Control, Large Language Models, and Image Classification with Activation as Entropy Constraints
Zilin Kang, Chonghua Liao, Tingqiang Xu, Huazhe Xu
https://arxiv.org/abs/2510.08549

Entropy Regularizing Activation: Boosting Continuous Control, Large Language Models, and Image Classification with Activation as Entropy Constraints
We propose ERA, a new paradigm that constrains the sampling entropy above given thresholds by applying specially designed activations to the outputs of models. Our approach demonstrates broad effectiveness across different domains: 1) for large language models(LLMs), boosting the AIME 2025 score for Qwen2.5-Math-7B by 37.4%; 2) for continuous control reinforcement learning agents, improving performance by more than 30% over strong baselines such as SAC on the challenging HumanoidBench; 3) for i…

Observational constraints on f(Q,T) gravity from the mass-radius relation and stability of compact stars
S. K. Maurya, Abdul Aziz, Ksh. Newton Singh, G. Mustafa, Y. Sekhmani, Saibal Ray
https://arxiv.org/abs/2510.06003

Observational constraints on f(Q,T) gravity from the mass-radius relation and stability of compact stars
In this investigation we examine the astrophysical consequences of the influence of pressure anisotropy on the physical properties of observed pulsars within the background of $f(Q,T)$ gravity by choosing a specific form $f(Q, T)=ψ_1\, Q + ψ_2 T$, where $ψ_1$ and $ψ_2$ are the model parameters. Initially, we solve the modified field equations for anisotropic stellar configurations by assuming the physically valid metric potential along with anisotropic function for the distribution of the i…

Empowering Prosumers: Incentive Design for Local Electricity Markets Under Generalized Uncertainty and Grid Constraints
P{\aa}l Forr Austnes, Matthieu Jacobs, Lu Wang, Mario Paolone
https://arxiv.org/abs/2510.12318

Empowering Prosumers: Incentive Design for Local Electricity Markets Under Generalized Uncertainty and Grid Constraints
Since the 1990s, widespread introduction of central (wholesale) electricity markets has been seen across multiple continents, driven by the search for efficient operation of the power grid through competition. The increase of renewables has made significant impacts both on central electricity markets and distribution-level grids as renewable power generation is often connected to the latter. These stochastic renewable technologies have both advantages and disadvantages. On one hand they offer v…

Generalized Multi-Constraint Extremum Seeking
Alan Williams, Jorge Cort\'es, Alexander Scheinker
https://arxiv.org/abs/2510.06403 https://arxiv.org/pdf…

Generalized Multi-Constraint Extremum Seeking
We generalize the Safe Extremum Seeking algorithm to address the minimization of an unknown objective function subject to multiple unknown inequality and equality constraints, relying on recent results of gradient flow systems. These constraints may represent safety or other critical conditions. The proposed ES algorithm functions as a general nonlinear programming tool, offering practical maintenance of all constraints and semiglobal practical asymptotic stability, utilizing a Lyapunov argumen…

Mean-Field Games with Constraints
Anran Hu, Zijiu Lyu
https://arxiv.org/abs/2510.11843 https://arxiv.org/pdf/2510.11843

Mean-Field Games with Constraints
This paper introduces a framework of Constrained Mean-Field Games (CMFGs), where each agent solves a constrained Markov decision process (CMDP). This formulation captures scenarios in which agents' strategies are subject to feasibility, safety, or regulatory restrictions, thereby extending the scope of classical mean field game (MFG) models. We first establish the existence of CMFG equilibria under a strict feasibility assumption, and we further show uniqueness under a classical monotonicity co…

Multiprobe constraints on early and late time dark energy
Alexander Reeves, Simone Ferraro, Andrina Nicola, Alexandre Refregier
https://arxiv.org/abs/2510.06114 https://

Multiprobe constraints on early and late time dark energy
We perform a multiprobe analysis combining cosmic microwave background (CMB) data from Planck and the Atacama Cosmology Telescope (ACT), ACT CMB lensing, and large-scale structure (LSS) measurements from the Dark Energy Spectroscopic Instrument (DESI), including DESI Legacy Imaging Survey (LS) galaxies and baryon acoustic oscillations (BAOs). We present the first $5\times2$pt analysis of ACT DR6 lensing, DESI LS, and Planck ISW. Within $Λ$CDM, this yields $S_8 = σ_8(Ω_m/0.3)^{0.5} = 0.819 \p…

What You Don't Know Can Hurt You: How Well do Latent Safety Filters Understand Partially Observable Safety Constraints?
Matthew Kim, Kensuke Nakamura, Andrea Bajcsy
https://arxiv.org/abs/2510.06492

What You Don't Know Can Hurt You: How Well do Latent Safety Filters Understand Partially Observable Safety Constraints?
Safe control techniques, such as Hamilton-Jacobi reachability, provide principled methods for synthesizing safety-preserving robot policies but typically assume hand-designed state spaces and full observability. Recent work has relaxed these assumptions via latent-space safe control, where state representations and dynamics are learned jointly through world models that reconstruct future high-dimensional observations (e.g., RGB images) from current observations and actions. This enables safety …

Decentralized CBF-based Safety Filters for Collision Avoidance of Cooperative Missile Systems with Input Constraints
Johannes Autenrieb, Mark Spiller
https://arxiv.org/abs/2510.06846

Decentralized CBF-based Safety Filters for Collision Avoidance of Cooperative Missile Systems with Input Constraints
This paper presents a decentralized safety filter for collision avoidance in multi-agent aerospace interception scenarios. The approach leverages robust control barrier functions (RCBFs) to guarantee forward invariance of safety sets under bounded inputs and high-relative-degree dynamics. Each effector executes its nominal cooperative guidance command, while a local quadratic program (QP) modifies the input only when necessary. Event-triggered activation based on range and zero-effort miss (ZEM…

Semi-infinite Nonconvex Constrained Min-Max Optimization
Cody Melcher, Zeinab Alizadeh, Lindsey Hiett, Afrooz Jalilzadeh, Erfan Yazdandoost Hamedani
https://arxiv.org/abs/2510.12007

Semi-infinite Nonconvex Constrained Min-Max Optimization
Semi-Infinite Programming (SIP) has emerged as a powerful framework for modeling problems with infinite constraints, however, its theoretical development in the context of nonconvex and large-scale optimization remains limited. In this paper, we investigate a class of nonconvex min-max optimization problems with nonconvex infinite constraints, motivated by applications such as adversarial robustness and safety-constrained learning. We propose a novel inexact dynamic barrier primal-dual algorith…

The value of storage in electricity distribution: The role of storage
Dirk Lauinger, Deepjyoti Deka, Sungho Shin
https://arxiv.org/abs/2510.12435 https://a…

The value of storage in electricity distribution: The role of storage
Electricity distribution companies deploy battery storage to defer grid upgrades by reducing peak demand. In deregulated jurisdictions, such storage often sits idle because regulatory constraints bar participation in electricity markets. Here, we develop an optimization framework that, to our knowledge, provides the first formal model of market participation constraints within storage investment and operation planning. Applying the framework to a Massachusetts case study, we find that market pa…

Heuristic Bundle Upper Bound Based Polyhedral Bundle Method for Semidefinite Programming
Zilong Cui, Ran Gu
https://arxiv.org/abs/2510.12374 https://arxiv.…

Heuristic Bundle Upper Bound Based Polyhedral Bundle Method for Semidefinite Programming
Semidefinite programming (SDP) is a fundamental class of convex optimization problems with diverse applications in mathematics, engineering, machine learning, and related disciplines. This paper investigates the application of the polyhedral bundle method to standard SDPs. The basic idea of this method is to approximate semidefinite constraints using linear constraints, and thereby transform the SDP problem into a series of quadratic programming subproblems. However, the number of linear constr…